HuggingCat

AI-driven Feline CKD Health App

Timeline: Jan-Apr 2026

Role: Recruiter/Outreach, UX Researcher, & Designer

Tools: Google Form, Spreadsheet, Fireflies.Ai, Figma, Canva, Google meets, Facebook, Reddit, & Instagram

With: Sav Shrestha (Sponsor/Mentor)

HuggingCat is an AI-driven health-tech app focused on improving how cat owners understand Chronic Kidney Disease (CKD) lab results and veterinary information. Working in a team of five, we researched and designed Bridging the gap between pet owners and veterinarians.

Overview

Understand how cat owners interpret CKD lab results and identify opportunities to improve clarity, usability, and communication between pet owners and veterinarians through an AI-driven system.

Goal

Process

Recruit Participants

Contacted 100+ users and 30+ veterinarians and researchers across multiple platforms. Organized outreach and tracking using spreadsheets and Google Forms.

Interviews & User Testing

Conducted interviews with 3 internal medicine researchers from Ohio State University, Texas A&M, and Oregon State University and 3+ veterinarians in the Austin metro area using.

Facilitated usability testing with 6+ cat owners (ages 20–55+), representing a range of technical experience levels and CKD severity. Sessions were recorded and analyzed using Fireflies.ai.

Cat: Sebastian (12 Yrs Old)

Participant #2

Internal Medicine Researcher

Ohio State University

“The app has strong potential, but improving medical accuracy, evidence-based recommendations, source verification, and clearer non-prescriptive communication would make the experience more trustworthy and clinically responsible for pet owners.”

Cat: Savannah (18.5 Yrs Old)

Participant #5

“It just makes it really easy to read and find useful and helpful information based on what your cat or you're going through.”

“I’m cautious on the AI aspect. It would be great if it could also list out where they got their sources.”

Feedback & Iteration

Synthesized findings from interviews and testing sessions, then compiled and presented insights to guide iterative product improvements.

Key Findings & Solution

Medical Accuracy & Trust

Evidence Across Stakeholders:

  • Veterinarians flagged misleading medical language and products.

  • Researchers questioned accuracy of clinical claims and sources.

  • Users expressed a need for reassurance and trust in AI outputs

Communication & Next Steps

Evidence Across Stakeholders:

  • Researchers: “What do I do next?" is missing from the experience.

  • Users felt uncertain after seeing abnormal results.

  • Vet communication often lacks structured follow-up guidance.

Design Response:

  • Provide actionable next-step guidance following lab result summaries.

  • Help users understand what actions to take next after reviewing results.

Data Clarity & Visual Hierarchy

Evidence Across Stakeholders:

  • Users struggled with equal weighting of lab values.

  • Researchers requested clearer prioritization of abnormal results.

  • Some confusion about how to interpret ranges and scores.

Design Response:

  • Provide a clear legend for color-coded indicators to support interpretation of lab values.

Usability & Long-Term Care Needs

Evidence Across Stakeholders:

  • Non-technical users had minor UI confusion (clickability, navigation).

  • Caregivers manually track symptoms, meds, and behavior.

  • Strong demand for an ongoing monitoring tool.

Design Response:

  • Add vet-reviewed content layer.

  • Improve citation transparency in articles.

  • Add vet-reviewed context and transparent user reviews.

Design Response:

  • Improve UI (clear buttons, clickable indicators).

  • Add symptom + medication tracking system.

  • Introduce health score + weekly summaries.

  • Support longitudinal care tracking.

Rebrand/Design System

Before

After

Future Steps & Reflection

Future steps on Hugging Cat will focus on adding daily health tracking tools for food intake, medication, hydration, and symptoms, with automated summaries to support long-term care insights. The AI system will be improved by adding citations to increase transparency. Future efforts will also explore collaboration with veterinarians to encourage owners to use the app as a supportive tool.

This project provided valuable experience working with veterinarians, researchers, and cat owners, all connected by a shared focus on improving cat care. These conversations deepened my understanding of both the medical complexity of CKD and the emotional challenges faced by caregivers. This project highlighted the importance of designing tools that balance clarity, accuracy, and empathy to better support both owners and veterinary professionals.